Anomalous events removal for automated traffic noise maps generation

Alsina-Pagès, Rosa Ma; Álías, Francesc; Socoró, Joan Claudi; Orga, Ferran; Benocci, R.; Zambon, G

doi:10.1016/j.apacoust.2019.03.007

Cited by 19 publications

(18 citation statements)

References 28 publications

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“…During the recordings, the presence of acoustic events non-related to road traffic (e.g., sirens, horns, works, dogs' barks, airplanes flyovers, etc.) may occur [13]. As a consequence, the collected acoustic data should be cleaned of these undesired events before feeding the noise map creation software [13] to avoid biasing the computation of the A-weighted equivalent sound levels (L Aeq ) beyond 2 dB, as recommended by the European Commission Working Group Assessment of Exposure to Noise (WG-AEN) [14].…”

Section: Introductionmentioning

confidence: 99%

“…may occur [13]. As a consequence, the collected acoustic data should be cleaned of these undesired events before feeding the noise map creation software [13] to avoid biasing the computation of the A-weighted equivalent sound levels (L Aeq ) beyond 2 dB, as recommended by the European Commission Working Group Assessment of Exposure to Noise (WG-AEN) [14]. In this context, the Signal-to-Noise Ratio (SNR) of these acoustic events becomes a crucial parameter to evaluate and model [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…During the last decade, several WASNs have been deployed in different smart cities such as Barcelona [25], Algemesí [26], Pisa [27], Monza [28], Halifax [29] and Milan and Rome [30] in Europe, or New York city [31], to name a few. In this WASN-based approach, the traditional manual cleaning of the Anomalous Noise Events (ANEs) on the noise pattern [32] becomes unfeasible due to the huge volume of data that have to be processed in real time [13]. As a consequence, the first generation of these WASN-based environmental noise monitoring systems have mainly been focused on measuring the global sound levels of the sensed locations, without considering the impact of the presence of specific acoustic events on the L Aeq computation.…”

Section: Introductionmentioning

confidence: 99%

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Aggregate Impact of Anomalous Noise Events on the WASN-Based Computation of Road Traffic Noise Levels in Urban and Suburban Environments

Álías

Orga

Alsina-Pagès

et al. 2020

Sensors

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Environmental noise can be defined as the accumulation of noise pollution caused by sounds generated by outdoor human activities, Road Traffic Noise (RTN) being the main source in urban and suburban areas. To address the negative effects of environmental noise on public health, the European Environmental Noise Directive requires EU member states to tailor noise maps and define the corresponding action plans every five years for major agglomerations and key infrastructures. Noise maps have been hitherto created from expert-based measurements, after cleaning the recorded acoustic data of undesired acoustic events, or Anomalous Noise Events (ANEs). In recent years, Wireless Acoustic Sensor Networks (WASNs) have become an alternative. However, most of the proposals focus on measuring global noise levels without taking into account the presence of ANEs. The LIFE DYNAMAP project has developed a WASN-based dynamic noise mapping system to analyze the acoustic impact of road infrastructures in real time based solely on RTN levels. After studying the bias caused by individual ANEs on the computation of the A-weighted equivalent noise levels through an expert-based dataset obtained before installing the sensor networks, this work evaluates the aggregate impact of the ANEs on the RTN measurements in a real-operation environment. To that effect, 304 h and 20 min of labeled acoustic data collected through the two WASNs deployed in both pilot areas have been analyzed, computing the individual and aggregate impacts of ANEs for each sensor location and impact range (low, medium and high) for a 5 min integration time. The study shows the regular occurrence of ANEs when monitoring RTN levels in both acoustic environments, which are especially common in the urban area. Moreover, the results reveal that the aggregate contribution of low- and medium-impact ANEs can become as critical as the presence of high-impact individual ANEs, thus highlighting the importance of their automatic removal to obtain reliable WASN-based RTN maps in real-operation environments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Aggregate Impact of Anomalous Noise Events on the WASN-Based Computation of Road Traffic Noise Levels in Urban and Suburban Environments

Álías

Orga

Alsina-Pagès

et al. 2020

Sensors

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“…Independent sources of sounds have been identified and highlighted to show how they exhibit differences both in time and frequency. These sounds spectrograms with another sensor can be found in [55,56], where a class II microphone was used to conduct the acquisition of the sounds. The analysis of characteristic sounds such as a siren and a motorbike depicts that they have the same spectrum and time distribution, despite them having been collected in different cities.…”

Section: Preliminary Results Of Data Acquisitionmentioning

confidence: 99%

Design of a Low-Cost Configurable Acoustic Sensor for the Rapid Development of Sound Recognition Applications

et al. 2020

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Concerned about the noise pollution in urban environments, the European Commission (EC) has created an Environmental Noise Directive 2002/49/EC (END) requiring Member states to publish noise maps and noise management plans every five years for cities with a high density of inhabitants, major roads, railways and airports. The END also requires the noise pressure levels for these sources to be presented independently. Currently, data measurements and the representations of the noise pressure levels in such maps are performed semi-manually by experts. This process is time and cost consuming, as well as limited to presenting only a static picture of the noise levels. To overcome these issues, we propose the deployment of Wireless Acoustic Sensor Networks with several nodes in urban environments that can enable the generation of real-time noise level maps, as well as detect the source of the sound thanks to machine learning algorithms. In this paper, we briefly review the state of the art of the hardware used in wireless acoustic applications and propose a low-cost sensor based on an ARM cortex-A microprocessor. This node is able to process machine learning algorithms for sound source detection in-situ, allowing the deployment of highly scalable sound identification systems.

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“…To that end, the DYNAMAP system was equipped with a dedicated algorithm embedded in the noise sensors for the detection of anomalous noise events (ANEs), i.e., noise events unrelated to road traffic noise [26][27][28]. Therefore, also the recorded data required to be manually cleaned up using the procedure described in Alsina-Pagés et al [29].…”

Section: Measuring Campaignmentioning

confidence: 99%

Dynamic Noise Mapping in the Suburban Area of Rome (Italy)

Benocci

Bellucci²,

Peruzzi³

et al. 2019

Environments

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A “real-time” noise mapping project, named DYNAMAP, has been developed in the framework of a Life+ 2013 program and cofunded by the European Commission. The project aims at giving a real picture of the noise generated by vehicular traffic. To this purpose, a dedicated platform has been developed to elaborate the information from distributed noise monitoring stations. The methodology has been implemented along the ring road encircling the city of Rome (Italy). A detailed description of the system is given together with a report on the testing campaign that allowed evaluation of the accuracy and reliability of the system. From the monitoring campaign satisfactory results have been achieved, showing an average overall prediction error of ~1.5 dB.

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Anomalous events removal for automated traffic noise maps generation

Cited by 19 publications

References 28 publications

Aggregate Impact of Anomalous Noise Events on the WASN-Based Computation of Road Traffic Noise Levels in Urban and Suburban Environments

Aggregate Impact of Anomalous Noise Events on the WASN-Based Computation of Road Traffic Noise Levels in Urban and Suburban Environments

Design of a Low-Cost Configurable Acoustic Sensor for the Rapid Development of Sound Recognition Applications

Dynamic Noise Mapping in the Suburban Area of Rome (Italy)

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